Supply Chain Risk Management (SCRM) Data Lead - (Clearance Required)

LMI Consulting, LLC; Arlington (VA); On-site; USD 120,000 - 185,000; Full time

Job summary

LMI Consulting, LLC seeks a Supply Chain Risk Management Data Lead in the US-VA Arlington area to design, develop, and implement enterprise SCRM analytics and data pipelines. You will bridge policy and technical execution, translating risk frameworks into data-driven dashboards and decision-support tools.

The role requires 7+ years in data engineering/science, proficiency in Python or R, SQL, and experience with enterprise data warehouses. Must hold a Top Secret clearance and US citizenship.

Qualifications

• Undergraduate degree in a quantitative discipline.
• 7+ years in data engineering/science, analytics, or quantitative modeling.
• Proficiency in Python, R, or similar languages.
• Experience building data pipelines and ETL/ELT processes.
• Proficiency with SQL and data warehouse architectures.
• Experience modeling and risk scoring; analytics leadership.
• Dashboards in Tableau/Power BI or equivalent.

Responsibilities

• Design, develop, and maintain data pipelines across multiple data sources.
• Define data schemas, governance, and quality processes for enterprise SCRM analytics.
• Develop quantitative risk models, supplier scoring, and segmentation frameworks.
• Apply NLP to extract risk signals from unstructured sources.
• Create modeling/simulation frameworks to assess disruption scenarios and wargaming tools.
• Build executive-ready dashboards and automated reporting for SCRM risk posture.

Skills

Python

R

SQL

Data analysis

Data storytelling

Education

Bachelor’s degree in a quantitative field

Tools

Snowflake

Databricks

Redshift

Synapse

Tableau

Power BI

AnyLogic

Job description

Supply Chain Risk Management (SCRM) Data Lead - (Clearance Required)

Job Locations

US-VA-Arlington

Overview

LMI is seeking a Supply Chain Risk Management (SCRM) Data and Analytics Lead to support the design, development, and implementation of an enterprise SCRM organization for a client in the Washington, DC metro area. The ideal candidate bridges SCRM policy and technical execution, with practitioner-level expertise in data engineering, data science, analytics, and modeling and simulation applied to supply chain risk contexts. This role serves as the technical counterpart to our SCRM strategy and policy capability, translating risk frameworks and business requirements into data pipelines, analytical models, dashboards, and decision-support tools that enable enterprise-wide SCRM operations.

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Headquartered in Tysons, Virginia, LMI serves the defense, space, healthcare, and energy sectors.

Responsibilities

• Design, develop, and maintain data pipelines that ingest, integrate, and transform supplier, contract, financial, and threat intelligence data from disparate internal and external sources.
• Define and implement data schemas, governance structures, and quality processes that support reliable, auditable SCRM analytics and reporting at enterprise scale.
• Develop quantitative supplier risk scoring models, criticality assessments, and risk segmentation frameworks using statistical and machine learning methods.
• Apply NLP and text analytics to extract risk signals from unstructured sources including news feeds, regulatory filings, and threat intelligence reports.
• Design and develop modeling and simulation frameworks to assess supply chain disruption scenarios, single-point-of-failure risks, and mitigation trade-offs, including scenario-based tools to support wargaming and executive decision-making.
• Build and maintain executive-ready dashboards, geospatial visualizations, and automated reporting pipelines that communicate SCRM risk posture clearly and actionably.
• Partner with SCRM strategy and policy experts to translate risk frameworks and governance requirements into technical data and analytical solutions.
• Define technical requirements for SCRM tools, platforms, APIs, and data integrations and support vendor evaluation and capability assessments.
• Facilitate working sessions with acquisition, cybersecurity, IT, data, and mission operations stakeholders to define data requirements, reporting needs, and analytical use cases.
• Prepare technical documentation, data dictionaries, model methodology briefs, and decision-ready products for both technical and senior non-technical audiences.

Qualifications

MINIMUM QUALIFICATIONS

• Undergraduate degree required; quantitative discipline preferred (data science, computer science, statistics, mathematics, operations research, or systems engineering).
• Seven (7) or more years of relevant experience in data engineering, data science, analytics, or quantitative modeling.
• Proficiency in Python, R, or similar languages for data manipulation, statistical analysis, and model development.
• Experience building and deploying data pipelines, ETL/ELT processes, or data integration solutions in enterprise environments.
• Proficiency with SQL and familiarity with data warehouse or data lake architectures (e.g., Snowflake, Databricks, Redshift, Synapse, or equivalent).
• Demonstrated experience developing quantitative risk models, supplier scoring frameworks, or anomaly detection capabilities.
• Experience designing and building dashboards and data visualizations in Tableau, Power BI, or equivalent platforms.
• Familiarity with SCRM concepts including supplier risk assessment, third-party due diligence, supplier segmentation, and criticality analysis.
• Strong written and verbal communication skills; ability to produce technical documentation and translate analytical findings into executive-ready outputs.
• Active TOP SECRET clearance required. Must be a U.S. citizen.

PREFERRED QUALIFICATIONS

• Experience with M&S tools or frameworks (e.g., AnyLogic, Simio, Arena, agent-based modeling platforms, or custom Python/R stochastic simulation development).
• Familiarity with federal SCRM policy frameworks including NIST SP 800-161, DFARS 252.204-7012, and related DoD acquisition risk guidance.
• Experience working in or supporting DoD or federal agency data environments, including familiarity with data classification, access controls, and ATO/RMF processes.
• Knowledge of graph analytics or geospatial analysis methods applied to supplier network mapping and geographic concentration risk.
• Familiarity with commercial supply chain risk data providers (e.g., Exiger, Sayari, Dun & Bradstreet) or open-source threat intelligence tools.

Target salary range

Target salary range: $120,000 - $185,000. Final compensation will be determined by a variety of factors including but not limited to your skills, experience, education, and/or certifications.

The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.

Equal Opportunity Employer Statement

LMI is an Equal Opportunity Employer. LMI is committed to the fair treatment of all and to our policy of providing applicants and employees with equal employment opportunities. LMI recruits, hires, trains, and promotes people without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, disability, age, protected veteran status, citizenship status, genetic information, or any other characteristic protected by applicable federal, state, or local law. If you are a person with a disability needing assistance with the application process, please contact [e-mail]

Colorado Residents: In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.

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